Abstract
In medical diagnostics, accurately classifying parasite species from microscopic images is challenging, especially in resource-limited areas. Our study presents a novel deep learning-based methodology that significantly enhances parasite classification accuracy in microscopic images by employing an image preprocessing technique where pixel values greater than a certain threshold are squared to enhance edge features. Using the Microscopic Images of Parasites Species dataset for testing, our approach shows exceptional performance across various parasites, overcoming obstacles like fecal impurities and blood smear variations. Our proposed method introduces “Accentuation Edge via Pixel Value Transformation” as a key innovation in the realm of parasite microscopic image classification. This edge accentuation aids deep learning models in achieving more accurate differentiation between parasitic and non-parasitic elements. Unlike traditional methods, our approach addresses previous limitations in sensitivity and specificity, leading to a notable improvement in classification performance. Our method demonstrated a groundbreaking 99.86% accuracy in parasite classification, marking a substantial advancement over existing microscopy and computational techniques. This method not only offers a scalable and effective solution for various clinical scenarios but also sets a new standard in the field of medical imaging and diagnosis of parasitic infections.
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References
Anorboev, A., Javokhir, M., Hong, J., Nguyen, N.T., Hwang, D.: Input image pixel interval method for classification using transfer learning. In: 2022 International Conference on Innovations in Intelligent Systems and Applications (INISTA), pp. 1–5. IEEE (2022)
Anorboev, A., Musaev, J., Hong, J., Nguyen, N.T., Hwang, D.: An image pixel interval power (IPIP) method using deep learning classification models. In: Asian Conference on Intelligent Information and Database Systems, pp. 196–208. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-21743-2_16
Anorboev, A., Musaev, J., Hong, J., Nguyen, N.T., Hwang, D.: SSTop3: Sole-Top-Three and Sum-Top-Three Class prediction ensemble method using deep learning classification models. In: International Conference on Computational Collective Intelligence, pp. 193–199. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-16210-7_15
Musaev, J., Anorboev, A., Phan, H.T., Hwang, D.: ETop3PPE: EPOCh’s Top-Three prediction probability ensemble method for deep learning classification models. In: Asian Conference on Intelligent Information and Database Systems, pp. 222–233. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-21743-2_18
Suzuki, C.T., Gomes, J.F., Falcao, A.X., Papa, J.P., Hoshino-Shimizu, S.: Automatic segmentation and classification of human intestinal parasites from microscopy images. IEEE Trans. Biomed. Eng. 60(3), 803–812 (2012)
Mayo, P., Anantrasirichai, N., Chalidabhongse, T.H., Palasuwan, D., Achim, A.: Detection of parasite eggs from microscopy images and the emergence of a new dataset (2022). arXiv preprint arXiv:2203.02940
Kundu, T.K., Anguraj, D.K.: A performance analysis of machine learning algorithms for malaria parasite detection using microscopic images. In: 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 980–984. IEEE (2023)
Das, D.K., Ghosh, M., Pal, M., Maiti, A.K., Chakraborty, C.: Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron 45, 97–106 (2013)
Zhang, C., et al.: Deep learning for microscopic examination of protozoan parasites. Comput. Struct. Biotechnol. J. 20, 1036–1043 (2022)
Ramarolahy, C., Gyasi, E.O., Crimi, A.: Classification and generation of microscopy images with Plasmodium falciparum via artificial neural networks (2020). bioRxiv, 2020-07
Anorboev, A., et al.: Ensemble of top3 prediction with image pixel interval method using deep learning. Comput. Sci. Inf. Syst., 56 (2023)
Saito, P.T., Suzuki, C.T., Gomes, J.F., de Rezende, P.J., Falcao, A.X.: Robust active learning for the diagnosis of parasites. Pattern Recogn. 48(11), 3572–3583 (2015)
Najgebauer, P., Grycuk, R., Rutkowski, L., Scherer, R., Siwocha, A.: Microscopic sample segmentation by fully convolutional network for parasite detection. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2019. LNCS (LNAI), vol. 11508, pp. 164–171. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20912-4_16
Ahmad, I., Shin, S.: A pixel-based encryption method for privacy-preserving deep learning models (2022). arXiv preprint arXiv:2203.16780
Lau, S.L., Lim, J., Chong, E.K., Wang, X.: Single-pixel image reconstruction based on block compressive sensing and convolutional neural network. Int. J. Hydromechatronics 6(3), 258–273 (2023)
Anorboev, A., Musaev, J., Hwang, D., Seo, Y.-S., Hong, J.: MICL-UNet: multi-input cross-layer UNet model for classification of diseases in agriculture. IEEE Access (2023)
Chang, Y., Chen, G., Chen, J.: Pixel-wise attention residual network for super-resolution of optical remote sensing images. Remote Sens. 15(12), 3139 (2023)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Li, S., Zhang, Y.: “Microscopic Images of Parasites Species”, Mendeley Data, V3 (2020). https://doi.org/10.17632/38jtn4nzs6.3
Trockman, A., Kolter, J.Z.: Patches are all you need?. arXiv preprint arXiv:2201.09792 (2022)
Musaev, J., Nguyen, N.T., Hwang, D.: Image channel as an input method for deep learning ensemble. In: International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1–5. IEEE (2021)
Katarzyniak, R.P., Nguyen, N.T.: Reconciling inconsistent profiles of agents’ knowledge states in distributed multiagent systems using consensus methods. Syst. Sci. 26(4), 93–119 (2000)
Duong T.H., Nguyen N.T., Jo G.S.: A method for integration of wordnet-based ontologies using distance measures. In: Proceedings of KES 2008. Lecture Notes in Artificial Intelligence, vol. 5177, pp. 210–219 (2018)
Nguyen, N.T.: Metody wyboru consensusu i ich zastosowanie w rozwiązywaniu konfliktów w systemach rozproszonych. Oficyna Wydawnicza Politechniki Wrocławskiej (2002)
Acknowledgments
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea Government (MSIT) (No. NRF-2023R1A2C1008134 and NRF-2022R1F1A1074641).
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Anorboev, A. et al. (2024). Enhancing Classification of Parasite Microscopy Images Through Image Edge-Accentuating Preprocessing. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2024. Lecture Notes in Computer Science(), vol 14796. Springer, Singapore. https://doi.org/10.1007/978-981-97-4985-0_11
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